Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

Kumar, Manish; Thenmozhi, M.

doi:10.1504/ijbaaf.2014.064307

Cited by 76 publications

(42 citation statements)

References 42 publications

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“…Regression using RF can be implemented for time series forecasting purposes. Representative applications can be found in many scientific fields including these of engineering [23,24], environmental and geophysical sciences [25][26][27], financial studies [28,29], and medicine [30], with varying performance. Furthermore, small datasets are used in these applications; therefore, the results cannot be generalized.…”

Section: Time Series Forecasting and Random Forestsmentioning

confidence: 99%

Variable Selection in Time Series Forecasting Using Random Forests

2017

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Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

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Section: Time Series Forecasting and Random Forestsmentioning

confidence: 99%

Variable Selection in Time Series Forecasting Using Random Forests

2017

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“…Ouzounis et al (2009) find that portfolios can generate different abnormal returns depending on the methodology employed. Any approach, however, must exhibit a hybrid which is useful in achieving high forecast accuracy and therefore better returns (Kuma and Thenmozhi, 2014). Delisted stocks are also included in the sample for the purpose of solving survivorship bias problems as the results of the study tend to skew higher (Kothari, Shanken and Sloan, 1995;Chui et al, 2003).…”

Section: Data Methodology and Hypothesesmentioning

confidence: 99%

Accounting analyses of momentum and contrarian strategies in emerging markets

Nnadi

Tanna

2017

Asia-Pacific Journal of Accounting & Economics

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We analyse the momentum and contrarian effects of stock markets in Brazil, Russia, India, China and South Africa (BRICS) using accounting data. The five markets show different characteristics with the Indian market having the strongest momentum effect. Stock markets in China and Brazil show significant short-term contrarian profit and intermediate to longterm momentum profit while South Africa shows short-term momentum effect and intermediate to long-term contrarian effect. The Russian stock market reveals largely insignificant momentum portfolio returns. We also find evidence that the contrarian profits in South Africa and China are caused by relatively high loser returns while positive momentum profit in India results from relatively high winner returns.

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“…In stock price prediction studies, some researches worked on engage ANN [9,10,11]. From statistical models' perspectives, autoregressive integrated moving average (ARIMA) models considered one of the most models extensively used in economics and finance fields [3], as well as stock forecasting [12,13,14,15]. However, the prediction of the stock market in time series considered one of the most challenging issues because of it volatile and noise features [16,17].…”

Section: From Artificial Intelligence Perspectives Artificial Neuralmentioning

confidence: 99%

ARIMA Model for Accurate Time Series Stocks Forecasting

Khan¹,

Alghulaiakh²

2020

IJACSA

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With the increasing of historical data availability and the need to produce forecasting which includes making decisions regarding investments, in addition to the needs of developing plans and strategies for the future endeavors as well as the difficulty to predict the stock market due to its complicated features, This paper applied and compared auto ARIMA (Auto Regressive Integrated Moving Average model). Two customize ARIMA(p,D,q) to get an accurate stock forecasting model by using Netflix stock historical data for five years. Between the three models, ARIMA (1,1,33) showed accurate results in calculating the MAPE and holdout testing, which shows the potential of using the ARIMA model for accurate stock forecasting.

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Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

Cited by 76 publications

References 42 publications

Variable Selection in Time Series Forecasting Using Random Forests

Variable Selection in Time Series Forecasting Using Random Forests

Accounting analyses of momentum and contrarian strategies in emerging markets

ARIMA Model for Accurate Time Series Stocks Forecasting

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